Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease.

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Tamoxifen offers long-term neuroprotection after hippocampal silent infarct in male rats.

Silent infarcts (SI) are a cerebral small vessel disease characterized by small subcortical infarcts. These occur in the absence of typical ischemia symptoms but are linked to cognitive decline and dementia. While there are no approved treatments for SI, recent results from our laboratory suggest that tamoxifen, a selective estrogen receptor modulator, is a viable candidate. In the present study, we induced SI in the dorsal hippocampal CA1 region of rats and assessed the effects of systemic administration of tamoxifen (5 mg/kg, twice) 21 days after injury on cognitive and pathophysiological measures, including cell loss, apoptosis, gliosis and estrogen receptors (ERs). We found that tamoxifen protected against the SI-induced cognitive dysfunction on the hippocampal-dependent, place recognition task, cell and ER loss, and increased apoptosis and gliosis in the CA1. Exploratory data analyses using a scatterplot matrix and principal component analysis indicated that SI-tamoxifen rats were indistinguishable from sham controls while they differed from SI rats, who were characterized by enhanced cell loss, apoptosis and gliosis, lower ERs, and recognition memory deficit. Supervised machine learning using support vector machine (SVM) determined predictors of progression from the early ischemic state to the dementia-like state. It showed that caspase-3 and ERα in the CA1 and exploration proportion were reliable and accurate predictors of this progression. Importantly, tamoxifen ameliorated SI-induced effects on all three of these variables, providing further evidence for its viability as a candidate treatment for SI and prevention of associated dementia.

The selective estrogen receptor modulator tamoxifen protects against subtle cognitive decline and early markers of injury 24 h after hippocampal silent infarct in male Sprague-Dawley rats.

Silent infarcts (SI) are subcortical cerebral infarcts occurring in the absence of typical ischemia symptoms and are linked to cognitive decline and dementia development. There are no approved treatments for SI. One potential treatment is tamoxifen, a selective estrogen receptor modulator. It is critical to establish whether treatments effectively target the early consequences of SI to avoid progression to complete injury. We induced SI in the dorsal hippocampal CA1 of rats and assessed whether tamoxifen is protective 24 h later against cognitive deficits and injury responses including gliosis, apoptosis, inflammation and changes in estrogen receptors (ERs). SI led to subtle cognitive impairment on the object place task, an effect ameliorated by tamoxifen administration. SI did not lead to detectable hippocampal cell loss but increased apoptosis, astrogliosis, microgliosis and inflammation. Tamoxifen protected against the effects of SI on all measures except microgliosis. SI increased ERα and decreased ERß in the hippocampus, which were mitigated by tamoxifen. Exploratory data analyses using scatterplot matrices and principal component analysis indicated that SI rats given tamoxifen were indistinguishable from controls. Further, SI rats were significantly different from all other groups, an effect associated with low levels of ERα and increased apoptosis, gliosis, inflammation, ERß, and time spent with the unmoved object. The results demonstrate that tamoxifen is protective against the early cellular and cognitive consequences of hippocampal SI 24 h after injury. Tamoxifen mitigates apoptosis, gliosis, and inflammation and normalization of ER levels in the CA1, leading to improved cognitive outcomes after hippocampal SI.

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

INTRODUCTION: Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This study investigated APOE4 proteome changes in people with AD, mild cognitive impairment, and no impairment. METHODS: Clinical, APOE genotype, and cerebrospinal fluid (CSF) proteome and AD biomarker data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Proteome profiling was done using supervised machine learning. RESULTS: We found an APOE4-specific proteome signature that was independent of cognitive diagnosis and AD pathological biomarkers, and increased risk of progression to cognitive impairment. Proteins were enriched in brain regions including the caudate and cortex and cells including endothelial cells, oligodendrocytes, and astrocytes. Enriched peripheral immune cells included T cells, macrophages, and B cells. DISCUSSION: APOE4 carriers have a unique CSF proteome signature associated with a strong brain and peripheral immune and inflammatory phenotype that likely underlies APOE4 carriers' vulnerability to cognitive decline and AD.

Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia. There is no treatment and AD models have focused on a small subset of genes identified in familial AD. Microarray studies have identified thousands of dysregulated genes in the brains of patients with AD yet identifying the best gene candidates to both model and treat AD remains a challenge. We performed a meta-analysis of microarray data from the frontal cortex (n = 697) and cerebellum (n = 230) of AD patients and healthy controls. A two-stage artificial intelligence approach, with both unsupervised and supervised machine learning, combined with a functional network analysis was used to identify functionally connected and biologically relevant novel gene candidates in AD. We found that in the frontal cortex, genes involved in mitochondrial energy, ATP, and oxidative phosphorylation, were the most significant dysregulated genes. In the cerebellum, dysregulated genes were involved in mitochondrial cellular biosynthesis (mitochondrial ribosomes). Although there was little overlap between dysregulated genes between the frontal cortex and cerebellum, machine learning models comprised of this overlap. A further functional network analysis of these genes identified that two downregulated genes, ATP5L and ATP5H, which both encode subunits of ATP synthase (mitochondrial complex V) may play a role in AD. Combined, our results suggest that mitochondrial dysfunction, particularly a deficit in energy homeostasis, may play an important role in AD.

The neuroprotective effects of estrogen and estrogenic compounds in spinal cord injury.

Spinal cord injury (SCI) occurs when the spinal cord is damaged from either a traumatic event or disease. SCI is characterised by multiple injury phases that affect the transmission of sensory and motor signals and lead to temporary or long-term functional deficits. There are few treatments for SCI. Estrogens and estrogenic compounds, however, may effectively mitigate the effects of SCI and therefore represent viable treatment options. This review systematically examines the pre-clinical literature on estrogen and estrogenic compound neuroprotection after SCI. Several estrogens were examined by the included studies: estrogen, estradiol benzoate, Premarin, isopsoralen, genistein, and selective estrogen receptor modulators. Across these pharmacotherapies, we find significant evidence that estrogens indeed offer protection against myriad pathophysiological effects of SCI and lead to improvements in functional outcomes, including locomotion. A STRING functional network analysis of proteins modulated by estrogen after SCI demonstrated that estrogen simultaneously upregulates known neuroprotective pathways, such as HIF-1, and downregulates pro-inflammatory pathways, including IL-17. These findings highlight the strong therapeutic potential of estrogen and estrogenic compounds after SCI.

A scalable, fully automated approach for regional quantification of immunohistochemical staining of astrocytes in the rat brain.

BACKGROUND: Astrocytes play a critical role in CNS functions by providing physiological support to surrounding cells. These cells present a particularly unique challenge for in vitro immunohistochemical quantification due reactive gliosis after insult or injury, which is characterized by the extension of long processes. NEW METHOD: We present an optimized QuPath protocol that is scalable, fully automated, and capable of being applied to images generated by whole slide scanning technology using this open-source software. RESULTS: We induced mechanical injury in the rat brain and stained astrocytes using glial fibrillary acidic protein (GFAP) and 3,3-diaminobenzidine (DAB) chromogen detection. Slides were scanned using a whole slide scanner, Vectra Polaris. Using QuPath, we summarize and contrast three ways of quantifying astrocytes in uninjured (contralateral) and injured (ipsilateral) hemispheres: optical density, positive pixels and positive proportion. COMPARISON WITH EXISTING METHODS: Robust quantification of DAB stained astrocytes remains elusive. Previous methodologies have relied on software that is not compatible with whole slide scanner images. Use of such software can compromise the data integrity within the image and is limited by issues with scalability and lack of automation. Previous methods using manual histopathological scoring are also limited by the ability to quantify large numbers of astrocytes. Given these limitations, we were unable to directly compare our method with those using other software or manual histopathology. CONCLUSIONS: Based on an analysis of our method, we conclude that positive proportion may be the most effective way to quantify astrocytic responses using GFAP and DAB immunohistochemistry in the brain.

Hippocampal silent infarct leads to subtle cognitive decline that is associated with inflammation and gliosis at twenty-four hours after injury in a rat model.

Silent infarcts (SI) are subcortical cerebral infarcts that occur in the absence of clinical symptoms commonly associated with ischemia and are linked to dementia development. Little is known about the pathophysiology underlying the cognitive dysfunction associated with SI, and few studies have examined the early cellular responses and neurobiological underpinnings. We induced SI in adult male Sprague-Dawley rats using an infusion of endothelin-1 in the CA1 dorsal hippocampus. Twenty-four hours later, we assessed cognition using the hippocampal-dependent object place recognition task. We also examined whether the resulting cognitive effects were associated with common markers of ischemia, specifically cell and synapse loss, gliosis, and inflammation, using histology and immunohistochemistry. Hippocampal SI led to subtle cognitive impairment on the object place recognition task 24 -hs post-injury. This was characterized by a significant difference in exploration proportion relative to a pre-injury baseline and a positive association between time spent with both the moved and unmoved objects. SI did not result in any detectable cell or synaptophysin loss, but did increase apoptosis, gliosis and inflammation in the CA1. Principal component analysis indicated the main variables associated with hippocampal SI included increased time spent with the unmoved object, gliosis, apoptosis and inflammation as well as decreased exploration proportion and CA1 cells. Our data demonstrate that hippocampal SI can lead to cognitive dysfunction 24 -hs after injury. Further, this appears to be driven by early degenerative processes including apoptosis, gliosis and inflammation, suggesting that these may be targets for early interventions treating hippocampal SI and its cognitive consequences.